class_docstr: Text Processor class implement NER, POS tag, lexical tree parsing.
  EnProcessor is designed by single instance mode.
docstr: Split a text into tokens (words, morphemes we can separate such as
  "n't", and punctuation).
  
  :param str text:
  :param bool is_one_sent:
  :param bool split_by_space:
  :return: list of tokens
docstr_tok: ["Split","a","text","into","tokens","(","words",",","morphemes","we","can","separate","such","as","''","n't","''",",","and","punctuation",")",".",":","param","str","text",":",":","param","bool","is_one_sent",":",":","param","bool","split_by_space",":",":","return",":","list","of","tokens"]
code: def tokenize(self, text, is_one_sent=False, split_by_space=False):
      """
      Split a text into tokens (words, morphemes we can separate such as
      "n't", and punctuation).
  
      :param str text:
      :param bool is_one_sent:
      :param bool split_by_space:
      :return: list of tokens
  
      """
      assert isinstance(text, str)
  
      def _tokenize_gen(text):
          if is_one_sent:
              yield self.tokenize_one_sent(
                  text, split_by_space=split_by_space
              )
          else:
              for sent in self.sentence_tokenize(text):
                  yield self.tokenize_one_sent(
                      sent, split_by_space=split_by_space
                  )
  
      return reduce(lambda x, y: x + y, list(_tokenize_gen(text)), [])
  
imports: import re
  import nltk
  import spacy
  import threading
  from functools import reduce
  from spacy.tokens import Doc
  from .nltk_res_load import *
  from ..settings import *
label_desc: Use image node_id 7 for calling the EnProcessor obj's underlying member method code with example usage: obj.tokenize(text, is_one_sent, split_by_space) and returns: reduce